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This paper demonstrates the utility of the implicit and explicit relational aspects across user con-tent to assess their quality. The quality variance in user-generated content is a major bottleneck to serving communities on online platforms. Depending on the edge semantics of each content graph, we embed its nodes via one of the above two mechanisms. Current content ranking methods primarily evaluate textual/non-textual features of each user post in isolation. We show that our contrastive operator creates discriminative magnification across the embeddings of competing posts. We exhaustively validate our method via accepted answer prediction over fifty diverse Stack-Exchanges with consistent relative gains of ∼5% accuracy over state-of-the-art neural, multi-relational and textual baselines. Third, we show a surprising result—applying classical boosting techniques to combine embeddings across the content graphs significantly outperforms the typical stacking, fusion, or neighborhood aggregation methods in graph convolutional architectures. Second, we develop two complementary graph convolutional operators that enable feature contrast for competing content and feature smoothing/sharing for similar content. First, we develop a modular platform-agnostic framework to represent the contrastive (or competing) and similarity-based relational aspects of user-generated content via independently induced content graphs.

Amazon Mechanical Turk, in terms of time, cost, quantity, and design of the data collection application. Personal digital photo libraries embody a large amount of in- formation useful for research into photo organization, photo layout, and development of novel photo browser features. We have collected a large, openly available photo feature dataset using this manner. Even when anonymity can be ensured, amassing a sizable dataset from these libraries is still difficult due to the visi- bility and Https://Go.Binaryoption.ae/JaunLB cost that would be required from such a study. More specifically, we compare and binary options discuss how it differs from common data collection methods, e.g. Our study with the Mac App Store suggests that popular application distribu- tion channels is a viable means to acquire massive data col- lections for binary options researchers. In 60 days, we collected data from 20,778 photo sets (473,772 photos). We illustrate the types of data that can be collected. We explore using the Mac App Store to reach more users to collect data from such personal digital photo libraries.

The emergence of the mediated social web—a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies – has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of "community". However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. The growth of online communities offers both opportunities and challenges for researchers and practitioners. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science. Participation in online communities has been observed to influence people’s behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications.

The therapist, using the quantitative measure and knowledge and observations, can adapt the feedback and physical environment of the AMRR system throughout therapy to address each participant’s individual impairments and progress. Individualized training plans, kinematic improvements measured over the entire therapy period, and the changes in relevant clinical scales and kinematic movement attributes before and after the month-long therapy are presented for 2 participants. The system provides real-time, intuitive, and integrated audio and visual feedback (based on detailed kinematic data) representative of goal accomplishment, activity performance, and body function during a reaching task. The AMRR system integrates traditional rehabilitation practices with state-of-the-art computational and motion capture technologies to create an engaging environment to train reaching movements. The substantial improvements made by both participants after AMRR therapy demonstrate that this system has the potential to considerably enhance the recovery of stroke survivors with varying impairments for both kinematic improvements and functional ability. The AMRR system also provides a quantitative kinematic evaluation that measures the deviation of the stroke survivor’s movement from an idealized, unimpaired movement. This article presents the principles of an adaptive mixed reality rehabilitation (AMRR) system, as well as the training process and results from 2 stroke survivors who received AMRR therapy, to illustrate how the system can be used in the clinic.